@article{zhang_zhu_huang_yu_sen huang_lopez-sanchez_devine_abdelhady_zheng_bulea_et al._2024, title={Actuator optimization and deep learning-based control of pediatric knee exoskeleton for community-based mobility assistance}, volume={97}, ISSN={["0957-4158"]}, url={https://doi.org/10.1016/j.mechatronics.2023.103109}, DOI={10.1016/j.mechatronics.2023.103109}, abstractNote={Lightweight and smart exoskeletons offer the potential to improve mobility in children. State-of-the-art pediatric exoskeletons are typically clinic-based since they are either tethered or portable but cumbersome and their design is often not optimized across a range of environments and users. To facilitate pediatric exoskeleton in community settings, we first proposed an actuator optimization framework that identified the optimal design parameters for both motor and transmission while minimizing the actuator mass and satisfying the output torque, speed, bandwidth, and resistance torque requirements. Guided by the optimization results, we customized a simple, lightweight actuator that met all mechatronic constraints for our portable exoskeleton (1.78 kg unilateral). Secondly, we adopted deep learning (Long Short Term Memory) based on gait phase estimation to facilitate stable control for community use. The models accurately estimated the gait phase on irregular walking patterns (accuracy 94.60%) without explicit training in children (typically developing and with cerebral palsy). The controller results demonstrated an elevated ability to adapt to the irregular gait patterns of the child with cerebral palsy. The experimental results in the child with typical development and four healthy adults demonstrated accurate assistive torque tracking performance (accuracy 97.00%) at different walking speeds (i.e., under uncertain torque to wearers). This work presented a holistic solution that includes both hardware innovation (actuator optimization framework) and software innovation (deep learning-based control) towards the paradigm shift of pediatric exoskeletons from clinic to community setting.}, journal={MECHATRONICS}, author={Zhang, Sainan and Zhu, Junxi and Huang, Tzu-Hao and Yu, Shuangyue and Sen Huang, Jin and Lopez-Sanchez, Ivan and Devine, Taylor and Abdelhady, Mohamed and Zheng, Minghui and Bulea, Thomas C. and et al.}, year={2024}, month={Feb} }
 @article{di lallo_yu_slightam_gu_yin_su_2024, title={Untethered Fluidic Engine for High-Force Soft Wearable Robots}, volume={6}, ISSN={["2640-4567"]}, DOI={10.1002/aisy.202400171}, abstractNote={Fluid‐driven artificial muscles exhibit a behavior similar to biological muscles which makes them attractive as soft actuators for wearable assistive robots. However, state‐of‐the‐art fluidic systems typically face challenges to meet the multifaceted needs of soft wearable robots. First, soft robots are usually constrained to tethered pressure sources or bulky configurations based on flow control valves for delivery and control of high assistive forces. Second, although some soft robots exhibit untethered operation, they are significantly limited to low force capabilities. Herein, an electrohydraulic actuation system that enables both untethered and high‐force soft wearable robots is presented. This solution is achieved through a twofold design approach. First, a simplified direct‐drive actuation paradigm composed of motor, gear‐pump, and hydraulic artificial muscle (HAM) is proposed, which allows for a compact and lightweight (1.6 kg) valveless design. Second, a fluidic engine composed of a high‐torque motor with a custom‐designed gear pump is created, which is capable of generating high pressure (up to 0.75 MPa) to drive the HAM in delivering high forces (580 N). Experimental results show that the developed fluidic engine significantly outperforms state‐of‐the‐art systems in mechanical efficiency and suggest opportunities for effective deployment in soft wearable robots for human assistance.}, journal={ADVANCED INTELLIGENT SYSTEMS}, author={Di Lallo, Antonio and Yu, Shuangyue and Slightam, Jonathon E. and Gu, Grace X. and Yin, Jie and Su, Hao}, year={2024}, month={Jun} }
 @article{yu_yang_huang_zhu_visco_hameed_stein_zhou_su_2023, title={Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction}, volume={1}, ISSN={["1573-9686"]}, url={https://publons.com/wos-op/publon/59334038/}, DOI={10.1007/s10439-023-03151-y}, abstractNote={Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm built on a two-stage artificial neural network. This work targets to develop an algorithm that can estimate and predict the gait cycle in real time using a portable controller with only two IMU sensors (one on each thigh) in the community setting. Our algorithm can detect the gait phase in unrhythmic conditions during walking, stair ascending, and stair descending, and classify these activities with standing. Moreover, our algorithm is able to predict both future intra- and inter-stride gait phases, offering a potential means to improve wearable device controller performance. The proposed data-driven algorithm is based on a dataset consisting of 5 able-bodied subjects and validated on 3 different able-bodied subjects. Under unrhythmic activity situations, validation shows that the algorithm can accurately identify multiple activities with 99.55% accuracy, and estimate ([Formula: see text]: 6.3%) and predict 200-ms-ahead ([Formula: see text]: 8.6%) the gait phase percentage in real time, which are on average 57.7 and 54.0% smaller than the error from the event-based method in the same conditions. This study showcases a solution to estimate and predict gait status for multiple unrhythmic activities, which may be deployed to controllers for wearable robots or health monitoring devices.}, journal={ANNALS OF BIOMEDICAL ENGINEERING}, author={Yu, Shuangyue and Yang, Jianfu and Huang, Tzu-Hao and Zhu, Junxi and Visco, Christopher J. and Hameed, Farah and Stein, Joel and Zhou, Xianlian and Su, Hao}, year={2023}, month={Jan} }
 @article{zhao_zuo_yu_gong_wang_sie_2023, title={Position-aware pushing and grasping synergy with deep reinforcement learning in clutter}, ISSN={["2468-2322"]}, DOI={10.1049/cit2.12264}, abstractNote={Abstract}, journal={CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY}, author={Zhao, Min and Zuo, Guoyu and Yu, Shuangyue and Gong, Daoxiong and Wang, Zihao and Sie, Ouattara}, year={2023}, month={Aug} }
 @article{yu_huang_di lallo_zhang_wang_fu_su_2022, title={Bio-inspired design of a self-aligning, lightweight, and highly-compliant cable-driven knee exoskeleton}, volume={16}, ISSN={["1662-5161"]}, DOI={10.3389/fnhum.2022.1018160}, abstractNote={Powered knee exoskeletons have shown potential for mobility restoration and power augmentation. However, the benefits of exoskeletons are partially offset by some design challenges that still limit their positive effects on people. Among them, joint misalignment is a critical aspect mostly because the human knee joint movement is not a fixed-axis rotation. In addition, remarkable mass and stiffness are also limitations. Aiming to minimize joint misalignment, this paper proposes a bio-inspired knee exoskeleton with a joint design that mimics the human knee joint. Moreover, to accomplish a lightweight and high compliance design, a high stiffness cable-tension amplification mechanism is leveraged. Simulation results indicate our design can reduce 49.3 and 71.9% maximum total misalignment for walking and deep squatting activities, respectively. Experiments indicate that the exoskeleton has high compliance (0.4 and 0.1 Nm backdrive torque under unpowered and zero-torque modes, respectively), high control bandwidth (44 Hz), and high control accuracy (1.1 Nm root mean square tracking error, corresponding to 7.3% of the peak torque). This work demonstrates performance improvement compared with state-of-the-art exoskeletons.}, journal={FRONTIERS IN HUMAN NEUROSCIENCE}, author={Yu, Shuangyue and Huang, Tzu-Hao and Di Lallo, Antonio and Zhang, Sainan and Wang, Tian and Fu, Qiushi and Su, Hao}, year={2022}, month={Nov} }